A Machine Learning Approach for Long-Term Prognosis of Bladder Cancer based on Clinical and Molecular Features

Title:

A Machine Learning Approach for Long-Term Prognosis of Bladder Cancer based on Clinical and Molecular Features

Link:

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233061/

Abstract:

Improving the consistency and reproducibility of bladder cancer prognoses necessitates the development of accurate, predictive prognostic models. Current methods of determining the prognosis of bladder cancer patients rely on manual decision-making, including factors with high intra- and inter-observer variability, such as tumor grade. To advance the long-term prediction of bladder cancer prognoses, we developed and tested a computational model to predict the 10-year overall survival outcome using population-based bladder cancer data, without considering tumor grade classification. The resulted predictive model demonstrated promising performance using a combination of clinical and molecular features, and was also strongly related to patient overall survival in Cox models. Our study suggests that machine learning methods can provide reliable long-term prognoses for bladder cancer patients, without relying on the less consistent tumor grade. If validated in clinical trials, this automated approach could guide and improve personalized management and treatment for bladder cancer patients.

Citation:

Qingyuan Song, John D. Seigne, Alan R. Schned, Karl T. Kelsey, Margaret R. Karagas, Saeed Hassanpour, “A Machine Learning Approach for Long-Term Prognosis of Bladder Cancer based on Clinical and Molecular Features”, American Medical Informatics Association (AMIA) Summits on Translational Science Proceedings, 2020:607-616, 2020.